【摘要】 为了提高短期电力负荷预测精度，该文提出了一种基于改进灰狼优化算法的TCN-BiGRU模型。输入序列先由改进后的时间卷积网络（TCN）捕捉长期依赖关系，再通过改进自注意力优化的双向门控循环单元（BiGRU）提取双向依赖关系。在模型内部结合AR模块、选举机制提升预测准确性，最后通过改进的灰狼优化算法优化TCN-BiGRU模型的参数以提升模型的综合性能。实验仿真表明，该模型的MAPE、MAE和RMSE分别为4.974%、0.029、0.034，均优于主流对照模型，有效提升了负荷预测精度。更多还原

【Abstract】 To improve the accuracy of short-term power load forecasting, this paper proposes a TCN-BiGRU model based on an improved grey wolf optimization algorithm. In this framework, the input sequence is first processed by an enhanced temporal convolutional network(TCN) to capture long-term dependencies, and then by an improved self-attention-optimized bidirectional gated recurrent unit(BiGRU) to extract bidirectional dependencies. an auto regression(AR) module and an election mechanism are integrated within the model to enhance forecasting accuracy. Finally, the model parameters of the TCN-BiGRU are optimized using the improved grey wolf optimization algorithm to further boost its overall performance. Experimental simulations demonstrate that the proposed model achieves a mean absolute percentage error(MAPE) of 4.974%, mean absolute error(MAE) of 0.029, and root mean square error(RMSE) of 0.034, outperforming mainstream benchmark models and effectively enhancing load forecasting accuracy.更多还原